A comparison of MERRA and NARR Reanalyses with the DOE ARM SGP data

Aaron D. Kennedy, Xiquan Dong, Baike Xi, Shaocheng Xie, Yunyan Zhang, Junye Chen

Research output: Contribution to journalArticle

100 Scopus citations

Abstract

Atmospheric states from the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the North American Regional Reanalysis (NARR) are compared with data from the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site, including theARMcontinuous forcing product and Cloud Modeling Best Estimate (CMBE) soundings, during the period 1999-2001 to understand their validity for single-column model (SCM) and cloud-resolving model (CRM) forcing datasets. Cloud fraction, precipitation, and radiation information are also compared to determine what errors exist within these reanalyses. For the atmospheric state, ARM continuous forcing and the reanalyses have good agreement with the CMBE sounding information, with biases generally within 0.5 K for temperature, 0.5 m s-1 for wind, and 5%for relative humidity. Larger disagreements occur in the upper troposphere (p<300 hPa) for temperature, humidity, and zonal wind, and in the boundary layer (p>800 hPa) for meridionalwind and humidity. In these regions, larger errorsmay exist in derived forcing products. Significant differences exist for vertical pressure velocity, with the largest biases occurring during the spring upwelling and summer downwelling periods. Although NARR and MERRA share many resemblances to each other, ARM outperforms these reanalyses in terms of correlation with cloud fraction. Because the ARM forcing is constrained by observed precipitation that gives the adequate mass, heat, and moisture budgets, much of the precipitation (specifically during the late spring/early summer) is caused by smaller-scale forcing that is not captured by the reanalyses. While reanalysis-based forcing appears to be feasible for the majority of the year at this location, it may have limited usage during the late spring and early summer, when convection is common at the ARMSGP site. Both NARR and MERRA capture the seasonal variation of cloud fractions (CFs) observed by ARM radar-lidar and Geostationary Operational Environmental Satellite (GOES) with high correlations (0.92-0.78) but with negative biases of 14% and 3%, respectively. Compared to the ARM observations, MERRA shows better agreement for both shortwave (SW) and longwave (LW) fluxes except for LW-down (due to a negative bias inwater vapor):NARRhas significant positive bias for SW-down and negative bias for LW-down under clear-sky and all-sky conditions. The NARR biases result from a combination of too few clouds and a lack of sufficient extinction by aerosols and water vapor in the atmospheric column. The results presented here represent only one location for a limited period, and more comparisons at different locations and longer periods are needed.

Original languageEnglish (US)
Pages (from-to)4541-4557
Number of pages17
JournalJournal of Climate
Volume24
Issue number17
DOIs
StatePublished - Sep 1 2011
Externally publishedYes

Keywords

  • Cloud resolving models
  • Fluxes
  • Forcings
  • Precipitation
  • Radiative fluxes

ASJC Scopus subject areas

  • Atmospheric Science

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